



import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()






def  lowerCamelCase_	(      lowerCAmelCase__	:	Union[str, Any]			,	lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	List[str]				)       ->							List[str]:

							'''simple docstring'''






							return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]






def  lowerCamelCase_	(      lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	Union[str, Any]			,	lowerCAmelCase__	:	Union[str, Any]			,	lowerCAmelCase__	:	str="attention"				)       ->							List[Any]:

							'''simple docstring'''






							A      						=    A      						=    np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]				)
							A      						=    k_tmp.reshape(k_tmp.shape[0]			,	k_tmp.shape[1] * k_tmp.shape[2]				)
							A      						=    np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]				)
							A      						=    o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]			,	o_tmp.shape[2]				)
							A      						=    np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]				)
							A      						=    q_tmp.reshape(q_tmp.shape[0]			,	q_tmp.shape[1] * q_tmp.shape[2]				)
							A      						=    np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]				)
							A      						=    v_tmp.reshape(v_tmp.shape[0]			,	v_tmp.shape[1] * v_tmp.shape[2]				)
							return k, o, q, v






def  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	Any=False				)       ->							str:

							'''simple docstring'''






							if split_mlp_wi:
														A      						=    params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
														A      						=    params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
														A      						=    (wi_a, wi_a)
							else:
														A      						=    params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

							A      						=    params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
							return wi, wo






def  lowerCamelCase_	(      lowerCAmelCase__	:	Any			,	lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	Any			,	lowerCAmelCase__	:	Dict				)       ->							Optional[int]:

							'''simple docstring'''






							return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]






def  lowerCamelCase_	(      lowerCAmelCase__	:	dict			,	*, lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	bool			,	lowerCAmelCase__	:	bool = False				)       ->							Optional[int]:

							'''simple docstring'''






							A      						=    traverse_util.flatten_dict(variables['target']				)
							A      						=    {'/'.join(lowerCAmelCase__				): v for k, v in old.items()}

							# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
							A      						=    'encoder/encoder/mlp/wi_0/kernel' in old
							print('Split MLP:'			,	lowerCAmelCase__				)

							A      						=    collections.OrderedDict()

							# Shared embeddings.
							A      						=    old['token_embedder/embedding']

							# Encoder.
							for i in range(lowerCAmelCase__				):
														# Block i, layer 0 (Self Attention).
														A      						=    tax_layer_norm_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'encoder'			,	'pre_attention_layer_norm'				)
														A						,   A						,   A						,   A      						=    tax_attention_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'encoder'			,	'attention'				)
														A      						=    layer_norm
														A      						=    k.T
														A      						=    o.T
														A      						=    q.T
														A      						=    v.T

														# Block i, layer 1 (MLP).
														A      						=    tax_layer_norm_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'encoder'			,	'pre_mlp_layer_norm'				)
														A						,   A      						=    tax_mlp_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'encoder'			,	lowerCAmelCase__				)
														A      						=    layer_norm
														if split_mlp_wi:
																					A      						=    wi[0].T
																					A      						=    wi[1].T
														else:
																					A      						=    wi.T
														A      						=    wo.T
														if scalable_attention:
																					# convert the rel_embedding of each layer
																					A      						=    tax_relpos_bias_lookup(
																					    lowerCAmelCase__			,	lowerCAmelCase__			,	'encoder'				).T

							A      						=    old['encoder/encoder_norm/scale']

							if not scalable_attention:
														A      						=    tax_relpos_bias_lookup(
														    lowerCAmelCase__			,	0			,	'encoder'				).T
														A      						=    tax_relpos_bias_lookup(
														    lowerCAmelCase__			,	0			,	'decoder'				).T

							if not is_encoder_only:
														# Decoder.
														for i in range(lowerCAmelCase__				):
																					# Block i, layer 0 (Self Attention).
																					A      						=    tax_layer_norm_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'decoder'			,	'pre_self_attention_layer_norm'				)
																					A						,   A						,   A						,   A      						=    tax_attention_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'decoder'			,	'self_attention'				)
																					A      						=    layer_norm
																					A      						=    k.T
																					A      						=    o.T
																					A      						=    q.T
																					A      						=    v.T

																					# Block i, layer 1 (Cross Attention).
																					A      						=    tax_layer_norm_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'decoder'			,	'pre_cross_attention_layer_norm'				)
																					A						,   A						,   A						,   A      						=    tax_attention_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'decoder'			,	'encoder_decoder_attention'				)
																					A      						=    layer_norm
																					A      						=    k.T
																					A      						=    o.T
																					A      						=    q.T
																					A      						=    v.T

																					# Block i, layer 2 (MLP).
																					A      						=    tax_layer_norm_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'decoder'			,	'pre_mlp_layer_norm'				)
																					A						,   A      						=    tax_mlp_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'decoder'			,	lowerCAmelCase__				)
																					A      						=    layer_norm
																					if split_mlp_wi:
																												A      						=    wi[0].T
																												A      						=    wi[1].T
																					else:
																												A      						=    wi.T
																					A      						=    wo.T

																					if scalable_attention:
																												# convert the rel_embedding of each layer
																												A      						=    tax_relpos_bias_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'decoder'				).T

														A      						=    old['decoder/decoder_norm/scale']

														# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
														if "decoder/logits_dense/kernel" in old:
																					A      						=    old['decoder/logits_dense/kernel'].T

							return new






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[int]			,	lowerCAmelCase__	:	bool				)       ->							int:

							'''simple docstring'''






							A      						=    collections.OrderedDict([(k, torch.from_numpy(v.copy()				)) for (k, v) in converted_params.items()]				)

							# Add what is missing.
							if "encoder.embed_tokens.weight" not in state_dict:
														A      						=    state_dict['shared.weight']

							if not is_encoder_only:
														if "decoder.embed_tokens.weight" not in state_dict:
																					A      						=    state_dict['shared.weight']

														if "lm_head.weight" not in state_dict:  # For old 1.0 models.
																					print('Using shared word embeddings as lm_head.'				)
																					A      						=    state_dict['shared.weight']

							return state_dict






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[int]			,	lowerCAmelCase__	:	Union[str, Any]			,	lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	int				)       ->							str:

							'''simple docstring'''






							A      						=    checkpoints.load_tax_checkpoint(lowerCAmelCase__				)
							A      						=    convert_tax_to_pytorch(
							    lowerCAmelCase__			,	num_layers=config.num_layers			,	is_encoder_only=lowerCAmelCase__			,	scalable_attention=lowerCAmelCase__				)
							A      						=    make_state_dict(lowerCAmelCase__			,	lowerCAmelCase__				)
							model.load_state_dict(lowerCAmelCase__			,	strict=lowerCAmelCase__				)






def  lowerCamelCase_	(      lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	Optional[Any]			,	lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	bool = False			,	lowerCAmelCase__	:	bool = False			,	)       ->							Dict:

							'''simple docstring'''






							A      						=    MTaConfig.from_json_file(lowerCAmelCase__				)
							print(F'''Building PyTorch model from configuration: {config}'''				)
							# Non-v1.1 checkpoints could also use T5Model, but this works for all.
							# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
							if is_encoder_only:
														A      						=    UMTaEncoderModel(lowerCAmelCase__				)
							else:
														A      						=    UMTaForConditionalGeneration(lowerCAmelCase__				)

							# Load weights from tf checkpoint
							load_tax_weights_in_ta(lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				)

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}'''				)
							model.save_pretrained(lowerCAmelCase__				)

							# Verify that we can load the checkpoint.
							model.from_pretrained(lowerCAmelCase__				)
							print('Done'				)


if __name__ == "__main__":
				__snake_case     :Any			       =argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
				# Required parameters
				parser.add_argument(
				    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
				)
				parser.add_argument(
				    '--config_file',
				    default=None,
				    type=str,
				    required=True,
				    help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
				)
				parser.add_argument(
				    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
				)
				parser.add_argument(
				    '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
				)
				parser.add_argument(
				    '--scalable_attention',
				    action='store_true',
				    help='Whether the model uses scaled attention (umt5 model)',
				    default=False,
				)
				__snake_case     :Dict			       =parser.parse_args()
				convert_tax_checkpoint_to_pytorch(
				    args.tax_checkpoint_path,
				    args.config_file,
				    args.pytorch_dump_path,
				    args.is_encoder_only,
				    args.scalable_attention,
				)
'''simple docstring'''




import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__UpperCAmelCase					=logging.get_logger(__name__)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    __lowerCamelCase            =    UniSpeechSatForSequenceClassification.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''projector.weight''']
    __lowerCamelCase            =    downstream_dict['''projector.bias''']
    __lowerCamelCase            =    downstream_dict['''model.post_net.linear.weight''']
    __lowerCamelCase            =    downstream_dict['''model.post_net.linear.bias''']
    return model






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Union[str, Any]:
    __lowerCamelCase            =    UniSpeechSatForAudioFrameClassification.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''model.linear.weight''']
    __lowerCamelCase            =    downstream_dict['''model.linear.bias''']
    return model






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       List[Any]:
    __lowerCamelCase            =    UniSpeechSatForXVector.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''connector.weight''']
    __lowerCamelCase            =    downstream_dict['''connector.bias''']
    for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
        __lowerCamelCase            =    downstream_dict[
            f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
        ]
        __lowerCamelCase            =    downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
    __lowerCamelCase            =    downstream_dict['''objective.W''']
    return model






@torch.no_grad()
def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    __lowerCamelCase            =    torch.load(UpperCamelCase__							,    map_location='''cpu'''							)

    __lowerCamelCase            =    checkpoint['''Downstream''']

    __lowerCamelCase            =    UniSpeechSatConfig.from_pretrained(UpperCamelCase__							)
    __lowerCamelCase            =    WavaVecaFeatureExtractor.from_pretrained(
        UpperCamelCase__							,    return_attention_mask=UpperCamelCase__							,    do_normalize=UpperCamelCase__							)

    __lowerCamelCase            =    hf_config.architectures[0]
    if arch.endswith('''ForSequenceClassification'''							):
        __lowerCamelCase            =    convert_classification(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    elif arch.endswith('''ForAudioFrameClassification'''							):
        __lowerCamelCase            =    convert_diarization(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    elif arch.endswith('''ForXVector'''							):
        __lowerCamelCase            =    convert_xvector(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    else:
        raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""							)

    if hf_config.use_weighted_layer_sum:
        __lowerCamelCase            =    checkpoint['''Featurizer''']['''weights''']

    hf_feature_extractor.save_pretrained(UpperCamelCase__							)
    hf_model.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  __UpperCAmelCase					=argparse.ArgumentParser()
  parser.add_argument(
      "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
  )
  parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
  parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
  __UpperCAmelCase					=parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
